code: def pad_target_tensor_for_fine_tuning(
      targets: Dict[str, torch.Tensor],
      predictions: Dict[str, torch.Tensor],
      model_inputs: torch.Tensor,
      of_name: str,
  ) -> Dict[str, torch.Tensor]:
      """Pad and adjust target tensors for fine-tuning LLMS models.
  
      This function is used to pad and adjust the target tensors with IGNORE_INDEX_TOKEN_ID based on the model inputs and
      predictions during the fine-tuning process of Language Models. Here's what this function does:
          1. If none of the tokens from the target were in the model inputs, we create a tensor of the length of model
              inputs with value IGNORE_INDEX_TOKEN_IDs. This ignores this row from affecting loss.
          2. If the target tokens were entirely inside the model inputs, we want to pad all the tokens in model_inputs
              coming from the input with IGNORE_INDEX_TOKEN_IDs and leave the target tokens as is. This ensures that all
              of the target tokens are used during loss computation.
          3. In the scenario that only some part of the target tokens were in the model inputs, we want to pad the model
              inputs until that point and only leave the partial tokens of the target as is. This ensures that we will
              only compute loss on the target tokens that were in the model inputs.
  
      Args:
          targets (Dict[str, torch.Tensor]): A dictionary containing the target tensors.
          predictions (Dict[str, torch.Tensor]): A dictionary containing the predicted tensors.
          model_inputs (torch.Tensor): The input tensor passed into the model's forward pass.
          of_name (str): The name of the target tensor to be padded and adjusted.
  
      Returns:
          Dict[str, torch.Tensor]: A dictionary containing the updated target
          dictionaries.
      """
      target_length = targets.get(of_name).size()[1]
      prediction_length = (
          predictions[of_name].get(PREDICTIONS).size()[1]
      )
  
      if target_length == prediction_length:
          return targets
  
      updated_targets = []
      for idx, target in enumerate(targets[of_name]):
          # Remove any leading IGNORE_INDEX_TOKEN_IDs in the target that were temporarily added for alignment
          end_index = (target != IGNORE_INDEX_TOKEN_ID).nonzero()[0]
          target = target[end_index:]
          target_device = target.device
  
          # See if any part of the target was in the tensor passed into the model's forward pass
          last_matching_index = find_last_matching_index(
              model_inputs[idx], target
          )
  
          # If the last matching index is -1, it means that the input tensor passed into the model was truncated
          # and did not contain the target tensor. In this case, we need to truncate the target tensors as well
          # and just set it to a tensor of IGNORE_INDEX_TOKEN_ID so that we don't compute loss on this target tensor.
          if last_matching_index == -1:
              updated_targets.append(
                  torch.full(
                      (prediction_length,), IGNORE_INDEX_TOKEN_ID
                  ).to(device=target_device)
              )
  
          # If the last matching index is not -1, it means that the input tensor passed into the model was not
          # truncated and contained either a part of the target tensor or the entire target tensor. In this case,
          # we need to set the target tensor to the part of the target tensor that was passed into the model while
          # also padding it to the correct length with IGNORE_INDEX_TOKEN_ID.
          else:
              padding = torch.full(
                  (last_matching_index,), IGNORE_INDEX_TOKEN_ID
              ).to(device=target_device)
              updated_targets.append(
                  torch.cat((padding, target), dim=-1)[
                      :prediction_length
                  ]
              )
  
      targets[of_name] = torch.stack(updated_targets).to(
          device=targets.get(of_name).device, dtype=torch.int64
      )
  
      return targets
  
imports: import copy
  import logging
  import tempfile
  from typing import Dict, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.nn.functional
  import transformers
  from bitsandbytes.nn.modules import Embedding
  from packaging import version
  from transformers import AutoConfig, AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizer, TextStreamer
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, PREDICTIONS, PROBABILITIES
  from ludwig.schema.trainer import LLMTrainerConfig
  from ludwig.utils.error_handling_utils import default_retry
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.model_utils import find_embedding_layer_with_path
label_desc: Use image node_id 10 for calling a global function with example usage: pad_target_tensor_for_fine_tuning(targets, predictions, model_inputs, of_name) and returns: targets, targets